1. Field of the Invention
The present invention relates to a method and system for computer-aided differential diagnosis of diseases, and in particular, computer-aided differential diagnosis using neural networks.
2. Discussion of the Background
Computer-aided differential diagnosis of diseases is an important subject in radiology. However, it is difficult to distinguish accurately between many diseases that can produce similar or even identical radiographic interstitial patterns. In such cases, a specific diagnosis can only be made by taking into consideration the multiple relevant clinical aspects of the case, i.e., age, sex, symptoms, etc., together with details of the radiographic findings. Thus, differential diagnosis of diseases lends itself to computer automation which can provide assistance to less expert observers of radiographic patterns, to bring the decision performance of these less expert observers to a level closer to that of experienced radiologists. However, computer-aided differential diagnosis of diseases has not gained wide acceptance due to the multiple clinical aspects of a case.
A powerful tool for use in solving problems involving pattern recognition and classification is an artificial neural network having a layered structure and applied with a supervised-learning procedure such as the back-propagating error correction algorithm as disclosed by Rumelhart et al "Parallel distributed processing" "Explorations in the Microstructure of Cognition"Cambridge: MIT Press (1986), Grossberg " Neural Network and Neural Intelligence" Cambridge: MIT Press (1988), and Eckmiller et al "Neural Computers" Berlin: Springer-Verlag (1989), which are herein incorporated by reference. Artificial neural networks consist of a number of neuron-like elements (units) and connections between them, and can be implemented by hardware and/or software. The units of the neural network are categorized into three types of different groups (layers) according to their functions as shown in FIG. 1. A first layer (input layer) is assigned to accepting a set of data representing an input pattern, a second layer (output layer) is assigned to provide a set of data representing an output pattern, and an arbitrary number of intermediate layers (hidden layers) convert the input pattern to the output pattern. Since the number of units in each layer is determined arbitrarily, the input layer and the output layer include sufficient numbers of units to represent the input patterns and output patterns, respectively, of a problem to be solved. For example, a neural network which is designed to distinguish between 9 types of diseases on the basis of 20 items of clinical information, should have 20 input units and 9 output units. However, the optimum number of hidden layers and associated units needs to be determined empirically.
Briefly, the principle of neural network can be explained in the following manner. Input data, which are represented by numbers ranging from 0 to 1, are supplied to input units of the neural network. Next, the output data are provided from output units through two successive nonlinear calculations (in a case of one hidden layer) in the hidden and output layers. The calculation at each unit in the layer, which is illustrated schematically in FIG. 2, excluding the input units, includes a weighted summation of all entry numbers, an addition of certain offset terms and a conversion into a number ranging from 0 to 1 using a sigmoid-shape function such as a logistic function. In FIG. 2, units labelled O.sub.1 to O.sub.n represent input or hidden units, W.sub.1 through W.sub.n represent the weighting factors assigned to each respective output from these input or hidden units, and I represents the summation of the outputs multiplied by the respective weighting factors. An output O is calculated using the logistic-function equation given where .theta. represents an offset value for the input I. The weighting factors and offset values are internal parameters of the neural network which are determined for a given set of input and output data.
Two different basic processes are involved in the neural network, namely, a training process and a testing process. The neural network is trained by the back-propagation algorithm using pairs of training input data and desired output data, as given by Rumelhart et al, Ibid, pp. 318-362. The internal parameters of the neural network are adjusted in order to minimize the difference between the actual outputs of the neural network and the desired outputs. By iteration of this procedure in a random sequence for the same set of input and output data, the neural network learns a relationship between the training input data and the desired output data. Once trained sufficiently, the neural network can distinguish different input data according to its learning experience. To date, the neural network approach has not been applied to the computer-aided differential diagnosis of interstitial diseases.